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Recursive Partitioning and Applications pp 133–162Cite as

Regression Trees and Adaptive Splines for a Continuous Response

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Abstract

The theme of this chapter is to model the relationship between a continuous response variable Y and a set of p predictors, x 1,…, x p , based on observations \(\left\{ {x_{i1},\cdots,x_{ip},Y_i } \right\}_1^N\). We assume that the underlying data structure can be described by \(Y = f(x_1,...,x_p ) + \varepsilon,\)where f is an unknown smooth function and e is the measurement error with mean zero but unknown distribution.

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Author information

Authors and Affiliations

  1. Department of Epidemiology and Public Health, Yale University School of Medicine, 60 College Street, New Haven, Connecticut, 06520-8034, USA

    Heping Zhang

  2. Emerging Pathogens Institute, University of Florida, 100009, Gainesville, FL, 32610, USA

    Burton H. Singer

Authors
  1. Heping Zhang
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  2. Burton H. Singer
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Correspondence to Heping Zhang .

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Zhang, H., Singer, B.H. (2010). Regression Trees and Adaptive Splines for a Continuous Response. In: Recursive Partitioning and Applications. Springer Series in Statistics, vol 0. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6824-1_10

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